Method and apparatus of recognizing field of semantic parsing information, device and readable medium

ABSTRACT

A method and apparatus of recognizing a field of semantic parsing information, a device and a readable medium. The method includes: obtaining at least one preset keyword extracting pattern which is in a preset field and used to parse user-input speech data to generate semantic parsing information, each of the at least one preset keyword extracting pattern; obtaining subject weights of keywords according to importance degree identifiers of the keywords in the preset keyword extracting patterns; calculating a subject score of the speech parsing information according to the subject weights of the keywords; recognizing whether the speech parsing information belongs to the preset field according to the subject score of the speech parsing information. The method recognizes the field to which the speech parsing information belongs to ensure correctness of the recognized field, and thereby ensure correctness of operations performed by the App according to the semantic parsing information.

The present application claims the priority of Chinese PatentApplication No. 2017103795771, filed on May 25, 2017, with the title of“Method and apparatus of recognizing field of semantic parsinginformation, device and readable medium”. The disclosure of the aboveapplications is incorporated herein by reference in its entirety.

FIELD OF THE DISCLOSURE

The present disclosure relates to the technical field of computerapplication, and particularly to a method and apparatus of recognizing afield of semantic parsing information, a device and a readable medium.

BACKGROUND OF THE DISCLOSURE

In the prior art, to better facilitate the user's use of an applicationApp, many applications all support speech input. As such, the user onlyneeds to input speech data to the App upon use, operations are simpleand use is convenient.

Specifically, in the prior art, after the user inputs speech data to theApp, the App needs to perform speech recognition for the speech datainput by the user, then uses one or more patterns in each preset fieldto perform semantic parsing for a speech recognition result, and obtainssemantic parsing information corresponding to the speech data in eachpreset field. That is to say, the semantic parsing information has thesame semantics as the speech data so that the App directly analyzes theuser's demands according to the sematic parsing information and therebyperforms corresponding operations. For example, the user may use thespeech data to request the application to invoke a certain application,or send a certain piece of verbal information, or query for a certainpiece of information, or query and reserve a hotel room or book anairplane ticket, or the like.

However, in the prior art, when the semantic parsing information isparsed, at least one segment pattern of a certain preset field might beused. However, the segment pattern includes less information quantityand cannot accurately identify a corresponding field so that areliability of the sematic parsing information belonging to the presetfield is lower. In the prior art, since the reliability of the parsedsematic parsing information belonging to the preset field is notverified, the field to which the parsed semantic parsing informationbelongs is wrong, and the App performs operations irrelevant to thefield of the speech data input by the user.

SUMMARY OF THE DISCLOSURE

The present disclosure provides a method and apparatus of recognizing afield of semantic parsing information, a device and a readable medium,to implement recognition of the field of the semantic parsinginformation.

The present disclosure provides a method of recognizing a field ofsemantic parsing information, the method comprising:

obtaining at least one preset keyword extracting pattern which is in apreset field and used to parse user-input speech data to generatesemantic parsing information, each of the at least one preset keywordextracting pattern comprising at least one keyword;

obtaining subject weights of keywords according to importance degreeidentifiers of the keywords in the preset keyword extracting patterns inthe preset field;

calculating a subject score of the speech parsing information accordingto the subject weights of the keywords;

recognizing whether the speech parsing information belongs to the presetfield according to the subject score of the speech parsing information.

Further optionally, before obtaining at least one preset keywordextracting pattern which is in a preset field and used to parseuser-input speech data to generate semantic parsing information, themethod further comprises:

setting a plurality of preset keyword extracting patterns in each saidpreset field, each of the preset keyword extracting patterns comprisingat least two keywords;

in the preset keyword extracting patterns of each said preset field,identifying importance degree identifiers of the keywords included inthe corresponding preset keyword extracting patterns in thecorresponding preset field.

Further optionally, in the method, before the step of, in the presetkeyword extracting patterns of each said preset field, identifyingimportance degree identifiers of the keywords included in thecorresponding preset keyword extracting patterns in the correspondingpreset field, the method further comprises:

obtaining importance degree identifiers of the keywords included in thepreset keyword extracting patterns in the corresponding preset field.

Further optionally, in the method, the obtaining importance degreeidentifiers of the keywords included in the preset keyword extractingpatterns in the corresponding preset field specifically comprises:

collecting several linguistic data in each preset field and generating acorresponding corpus of the preset field;

performing word segmentation for linguistic data in the corpus, andextracting valid segmented words in the respective linguistic data asthe keywords included by the linguistic data;

making statistics of a frequency of occurrence of the keyword in allkeywords obtained after word segmentation is performed for severallinguistic data in the corpus, as a word frequency of the correspondingkeyword in the corpus;

setting an importance degree identifier in the preset field for acorresponding keyword according to a probability of the word frequencyof the keyword in the corpus in an occurrence frequency of all thekeywords obtained after word segmentation is performed for severallinguistic data.

Further optionally, in the method, the obtaining subject weights ofkeywords according to importance degree identifiers of the keywords inthe preset keyword extracting patterns in the preset field specificallycomprises:

if the importance degree identifier of the keyword in the preset keywordextracting pattern in the preset field is high, obtaining a 0 subjectweight corresponding to high, according to a correspondence relationshipbetween the importance degree identifier and the subject weight;

if the importance degree identifier of the keyword in the preset keywordextracting pattern in the preset field is middle, obtaining a subjectweight corresponding to middle as a first prime number according to thecorrespondence relationship between the importance degree identifier andthe subject weight; or

if the importance degree identifier of the keyword in the preset keywordextracting pattern in the preset field is low, obtaining a subjectweight corresponding to low as a second prime number according to thecorrespondence relationship between the importance degree identifier andthe subject weight; the second prime number is not equal to the firstprime number; the importance degree of the keyword identified with highin the preset field is higher than that of the keyword identified withmiddle in the preset field; the importance degree of the keywordidentified with middle in the preset field is higher than that of thekeyword identified with low in the preset field.

Further optionally, in the method, the calculating a subject score ofthe speech parsing information according to the subject weights of thekeywords specifically comprises:

multiplying subject weights of the keywords, to obtain the subject scoreof the speech parsing information.

Further optionally, in the method, the recognizing whether the speechparsing information belongs to the preset field according to the subjectscore of the speech parsing information specifically comprises:

if the subject score of the speech parsing information is 0, determiningthat the speech parsing information belongs to the preset field; or

if the subject score of the speech parsing information minus a firstparameter or a second parameter to get a remainder 0, determining thatthe speech parsing information includes the keyword with the middleimportance degree identifier and that the number of the includedkeywords is larger than 1, and determining that the speech parsinginformation belongs to the preset field; wherein the first parameter isequal to a square of the first prime number, and the second parameter isequal to a product of the first prime number and the second primenumber; or

if the subject score of the speech parsing information is not equal to 0and the remainder resulting from the subject score minus the firstparameter or second parameter is not equal to 0, determining that thespeech parsing information does not belong to the preset field.

The present disclosure provides an apparatus of recognizing a field ofsemantic parsing information, the apparatus comprising:

a pattern obtaining module configured to obtain at least one presetkeyword extracting pattern which is in a preset field and used to parseuser-input speech data to generate semantic parsing information, each ofthe at least one preset keyword extracting pattern comprising at leastone keyword;

a subject weight obtaining module configured to obtain subject weightsof keywords according to importance degree identifiers of the keywordsin the preset keyword extracting patterns in the preset field;

a calculating module configured to calculate a subject score of thespeech parsing information according to the subject weights of thekeywords;

a recognizing module configured to recognize whether the speech parsinginformation belongs to the preset field according to the subject scoreof the speech parsing information.

Further optionally, in the apparatus, the apparatus further comprises:

a setting module configured to set a plurality of preset keywordextracting patterns in each said preset field, each preset keywordextracting pattern comprising at least two keywords;

an importance degree identifying module configured to, in the presetkeyword extracting patterns of each said preset field, identifyimportance degree identifiers of the keywords included in thecorresponding preset keyword extracting patterns in the correspondingpreset field.

Further optionally, in the apparatus, the apparatus further comprises:

an importance degree identifier obtaining module configured to obtainimportance degree identifiers of the keywords included in the presetkeyword extracting patterns in the corresponding preset field.

Further optionally, in the apparatus, the importance degree identifierobtaining module is specifically configured to:

collect several linguistic data in each preset field and generate acorresponding corpus of the preset field;

perform word segmentation for linguistic data in the corpus, and extractvalid segmented words in the respective linguistic data as the keywordsincluded by the linguistic data;

make statistics of a frequency of occurrence of the keyword in allkeywords obtained after word segmentation is performed for severallinguistic data in the corpus, as a word frequency of the correspondingkeyword in the corpus;

set an importance degree identifier in the preset field for acorresponding keyword according to a probability of the word frequencyof the keyword in the corpus in an occurrence frequency of all thekeywords obtained after word segmentation is performed for severallinguistic data.

Further optionally, in the apparatus, the subject weight obtainingmodule is specifically configured to:

if the importance degree identifier of the keyword in the preset keywordextracting pattern in the preset field is high, obtain a 0 subjectweight corresponding to high, according to a correspondence relationshipbetween the importance degree identifier and the subject weight;

if the importance degree identifier of the keyword in the preset keywordextracting pattern in the preset field is middle, obtain a subjectweight corresponding to middle as a first prime number according to thecorrespondence relationship between the importance degree identifier andthe subject weight; or

if the importance degree identifier of the keyword in the preset keywordextracting pattern in the preset field is low, obtain a subject weightcorresponding to low as a second prime number according to thecorrespondence relationship between the importance degree identifier andthe subject weight; the second prime number is not equal to the firstprime number; the importance degree of the keyword identified with highin the preset field is higher than that of the keyword identified withmiddle in the preset field; the importance degree of the keywordidentified with middle in the preset field is higher than that of thekeyword identified with low in the preset field.

Further optionally, in the apparatus, the calculating module isspecifically configured to:

multiply subject weights of the keywords, to obtain the subject score ofthe speech parsing information.

Further optionally, in the apparatus, the recognizing module isspecifically configured to:

if the subject score of the speech parsing information is 0, determinethat the speech parsing information belongs to the preset field; or

if the subject score of the speech parsing information minus a firstparameter or a second parameter to get a remainder 0, determine that thespeech parsing information includes the keyword with the middleimportance degree identifier and that the number of the includedkeywords is larger than 1, and determine that the speech parsinginformation belongs to the preset field; wherein the first parameter isequal to a square of the first prime number, and the second parameter isequal to a product of the first prime number and the second primenumber; or

if the subject score of the speech parsing information is not equal to 0and the remainder resulting from the subject score minus the firstparameter or second parameter is not equal to 0, determine that thespeech parsing information does not belong to the preset field.

The present disclosure further provides a computer device, comprising:

one or more processors,

a memory for storing one or more programs,

the one or more programs, when executed by said one or more processors,enabling said one or more processors to implement the above-mentionedmethod of recognizing the field of the sematic parsing information.

The present disclosure further provides a computer readable medium onwhich a computer program is stored, the program, when executed by aprocessor, implementing the above-mentioned method of recognizing thefield of the sematic parsing information.

According to the method and apparatus of recognizing the field of thesematic parsing information, the device and the readable medium of thepresent disclosure, at least one preset keyword extracting pattern whichis in a preset field and used to parse user-input speech data togenerate semantic parsing information is obtained, wherein each of theat least one preset keyword extracting pattern comprises at least onekeyword; subject weights of keywords are obtained according to theimportance degree identifiers of keywords in the keyword extractingpatterns in the preset field; a subject score of speech parsinginformation is calculated according to the subject weights of thekeywords; whether the speech parsing information belongs to the presetfield is recognized according to the subject score of the speech parsinginformation. The technical solution of the present disclosure may beemployed to recognize the field to which the speech parsing informationbelongs to thereby ensure correctness of the recognized field of thespeech parsing information, and thereby ensure correctness of operationsperformed by the App according to the semantic parsing information.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a flow chart of an embodiment of a method of recognizing afield of semantic parsing information according to the presentdisclosure.

FIG. 2 is a structural diagram of a first embodiment of an apparatus ofrecognizing a field of semantic parsing information according to thepresent disclosure.

FIG. 3 is a structural diagram of a second embodiment of an apparatus ofrecognizing a field of semantic parsing information according to thepresent disclosure.

FIG. 4 is a structural diagram of an embodiment of a computer deviceaccording to the present disclosure.

FIG. 5 is an example diagram of a computer device according to thepresent disclosure.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The present disclosure will be described in detail in conjunction withfigures and specific embodiments to make objectives, technical solutionsand advantages of the present disclosure more apparent.

FIG. 1 is a flow chart of an embodiment of a method of recognizing afield of semantic parsing information according to the presentdisclosure. As shown in FIG. 1, the method of recognizing a field ofsematic parsing information according to the present embodiment mayspecifically include the following steps:

100: obtaining at least one preset keyword extracting pattern which isin a preset field and used to parse user-input speech data to generatesemantic parsing information, wherein each of the at least one presetkeyword extracting pattern comprises at least one keyword;

A subject of executing method of recognizing a field of sematic parsinginformation according to the present embodiment is an apparatus ofrecognizing a field of semantic parsing information. The apparatus ofrecognizing a field of semantic parsing information may be used in anyapplication or platform capable of receiving the user's speech input.

When the method of recognizing a field of semantic parsing informationof the present embodiment is used, the user-input speech data isreceived first, and then the user-input speech data is parsed togenerate semantic parsing information. Specifically, it is necessary tofirst perform speech recognition for the user's speech data to obtainverbal information corresponding to the speech data, and then performsemantic parsing for the verbal information corresponding to the speechdata to obtain the sematic parsing information. In practicalapplication, the user-input speech data might belong to variousdifferent fields such as science and technology, education, recreation,hotel and train tickets. To perform correct semantic parsing for thespeech data, it is feasible to preset keyword extracting patterns in aplurality of preset fields, and set a plurality of preset keywordextracting patterns in each preset field. In the present embodiment,when semantic parsing is performed, it is specifically feasible toemploy one, two or more preset keyword extracting patterns in eachpreset field, extract one or more keywords from the verbal informationcorresponding to the speech data, and generate the sematic parsinginformation in a preset format. Finally, one semantic parsinginformation is generated in each preset field, but it cannot be ensuredthe sematic parsing information of each field is correct.

The keyword of the present embodiment may also be called a term, and thepreset keyword extracting pattern may also be called a preset termextracting pattern. The preset term extracting pattern may include aslot of at least one term. When semantic parsing is performed, the typeof the employed preset term extracting pattern may be a precise patternor a segment pattern. In practical application, the precise pattern mayinclude slots of a plurality of terms, the generated semantic parsinginformation is very accurate, and it is usually feasible to determinethe field corresponding to the semantic parsing information, namely, afield to which the preset term extracting pattern corresponding to theprecise pattern as the type used to generate the semantic parsinginformation, needless to perform field recognition any more. The segmentpattern includes less information quantity, for example, the segmentpattern may only include one piece of time information, priceinformation or the like. If one, two or more segment patterns of thepreset field are employed when the semantic parsing is performed, thefield of the semantic parsing information obtained from the parsing atthis time might not be accurate, whereupon the technical solution of thepresent embodiment needs to be employed to recognize the field of thesematic parsing information. For example, according to segment patternsin the field of hotels, if the sematic parsing information obtained byparsing with a segment pattern carrying time and a segment patterncarrying a price is “I spent 25 yuan yesterday afternoon”, obviously thesemantic parsing information should not belong to the field of hotels.Hence, in the present embodiment it is feasible to perform recognitionfor the field of the semantic parsing information in this case and thenfilter away semantic parsing information whose field is obviously wrong.

As known from the above analysis, in the present embodiment, the methodof recognizing a field of semantic parsing information is mainlydirected to the situation that upon semantic parsing, a segment patternin a preset field is employed to generate semantic parsing information.If the solution of the present embodiment does not have any advantagewith regard to the situation that a precise pattern in a preset field isemployed to generate semantic parsing information upon semantic parsing,it is unnecessary to perform field recognition any more. Therefore,optionally, before step 100, the method further comprises: judgingwhether the type of the preset keyword extracting pattern used upongenerating the semantic parsing information includes a segment pattern;if yes, using the method of the embodiment of the present disclosure torecognize the field of the semantic parsing information. Or, it is alsofeasible to obtain the number of preset keyword extracting patternsemployed upon generating the semantic parsing information; then judgewhether the number of the preset keyword extracting patterns is largerthan 1. If the number of larger than 1, the precise pattern might not beemployed, whereupon step 100 begins to be performed to performrecognition for the field of the semantic parsing information; if thenumber is equal to 1, it is further necessary to judge whether thepreset keyword extracting pattern is the precise pattern or the segmentpattern; if the preset keyword extracting pattern is the precisepattern, since the precise pattern is preset for the user according tothe keyword of the preset field and can precisely represents the presetfield, it is unnecessary to subsequently recognize the field of thesemantic parsing information when the precise pattern is employed whenthe semantic parsing information is parsed. If the preset keywordextracting pattern is the segment pattern, step 100 begins to beperformed to perform recognition with the method of recognizing a fieldof semantic parsing information of the present embodiment.

In addition, optionally, each preset keyword extracting pattern of eachpreset field of the present embodiment not only comprises at least onekeyword but also may comprise an importance degree identifier of eachkeyword. Specifically, it is possible to identify the importance degreeidentifier of the keyword on each keyword in the preset keywordextracting pattern.

For example, optionally, before the step 100 “obtaining at least onepreset keyword extracting pattern which is in a preset field and used toparse user-input speech data to generate semantic parsing information”,the method may further comprise the following steps:

(a1) setting a plurality of preset keyword extracting patterns in eachpreset field, each preset keyword extracting pattern comprising at leasttwo keywords;

For example, in the field of hotels, one preset keyword extractingpattern is set to include three keywords “reserve”, “tomorrow” and“hotel”; another preset keyword extracting pattern is set to includekeywords “reserve”, “holiday” and “hotel”; a further preset keywordextracting pattern is set to include keywords “hotel”, “check-in” and“handle” and the like.

The plurality of preset keyword extracting patterns of each preset fieldof the present embodiment may be manually set by the user according to aspeech data input habit in the preset field. For example, the apparatusof recognizing a field of semantic parsing information may receive thepreset keyword extracting patterns in each preset field input by theuser through a man-machine interface pattern. The man-machine interfacepattern may include a mouse and/or a keyboard, or may be a touch screendetecting device. The touch screen detecting device detects and receivesthe user-input preset keyword extracting patterns in each preset field,and stores them in the apparatus of recognizing a field of semanticparsing information.

(a2) in the preset keyword extracting patterns of each preset field,identifying importance degree identifiers of keywords included in thecorresponding preset keyword extracting patterns in the correspondingpreset field.

That is to say, in the preset keyword extracting patterns are identifiedan importance degree identifier of each keyword in the preset field towhich the preset keyword extracting pattern belongs.

For example, before step (a2), the method may further comprise thefollowing step: obtaining importance degree identifiers of keywordsincluded in the preset keyword extracting patterns in the correspondingpreset field.

Furthermore, “obtaining importance degree identifiers of keywordsincluded in the preset keyword extracting patterns in the correspondingpreset field” may specifically comprise the following steps:

(b1) collecting several linguistic data in each preset field andgenerating a corresponding corpus of the preset field;

Several linguistic data in each preset field may be collected, and eachlinguistic data may be semantic parsing information corresponding to theuser-input speech data in the preset field. In each preset field, thecorpus of the preset field may be generated by collecting severallinguistic data.

(b2) performing word segmentation for linguistic data in the corpus, andextracting valid segmented words in the respective linguistic data askeywords included by the linguistic data;

Word segmentation is performed for each linguistic data to obtain aplurality of segmented words, and then meaningless segmented words, forexample person words such as “you”, “I”, “you” and “we” and words suchas “

(de)”, “

(di)”, “

(a)”, “

(ni)” and “

(ma)” without practical meaning, may be removed, and only the remainingvalid words are retained as keywords of the linguistic data

(b3) making statistics of a frequency of occurrence of each keyword inall keywords obtained after word segmentation is performed for severallinguistic detain the corpus, as a word frequency of the correspondingkeyword in the corpus;

It is feasible to, in this manner, obtain keywords included in eachlinguistic data in the corpus, and then make statistics of a frequencyof occurrence of all keywords obtained after word segmentation isperformed for all linguistic data in the corpus and a word frequency ofeach keyword in the corpus.

(b4) setting an importance degree identifier in a preset field for acorresponding keyword according to a probability of the word frequencyof each keyword in the corpus in an occurrence frequency of all keywordsobtained after word segmentation is performed for several linguisticdata.

For example, it is feasible to divide the word frequency of each keywordin the corpus by the occurrence frequency of all keywords obtained afterword segmentation is performed for several linguistic data, to obtainthe probability of occurrence of the keyword in the linguistic data inthe corpus. For example, if the word frequency of a certain keyword inthe corpus is 100 times, and the occurrence frequency of all keywordsobtained after word segmentation is performed for several linguisticdata in the corpus is 2000 times, the probability of occurrence of thekeyword in the linguistic data in the corpus is equal to100/2000=1/20=0.05. The probability of occurrence of each keyword in thelinguistic data in the corpus may be obtained in this manner. Then, theimportance degree identifier in the preset field is set for thecorresponding keyword according to the probability of occurrence of eachkeyword in the linguistic data in the corpus. A keyword with acorresponding large probability may be set as having an importantimportance degree identifier in the preset field; a keyword with a smallprobability may be set as having an unimportant importance degreeidentifier in the preset field. For example, the importance degrees maybe classified into three classes: a keyword with a probability largerthan or equal to a first preset threshold is set as having a highimportance degree identifier in the preset field; a keyword with aprobability larger than or equal to a second preset threshold andsmaller than the first preset threshold is set as having a middleimportance degree identifier in the preset field; a keyword with aprobability smaller than the second preset threshold is set as having alow importance degree identifier in the preset field. Alternatively, itis also feasible to only set two importance degree identifiers among thehigh, middle and low importance degree identifiers. The importancedegree identifier which is not set is considered as the third importancedegree identifier by default. For example, only high and middleimportance degree identifiers are set for keywords, and other keywordsare considered as having a low importance degree identifier by default.

Alternatively, in the present embodiment, it is also possible todirectly employ the probability of occurrence of the keyword in thelinguistic data in the corpus, as the importance degree identifier ofthe keyword in the preset field. A larger probability indicates a higherimportance degree of the keyword in the preset field.

Finally, in the preset keyword extracting patterns in the preset fieldsare identified importance degree identifiers of keywords included in thecorresponding preset keyword extracting patterns in the correspondingpreset fields.

101: obtaining subject weights of keyword according to the importancedegree identifiers of keywords in the keyword extracting patterns in thepreset field;

As known from the manner of the above embodiment, in the keywordextracting patterns in the preset fields are identified importancedegree identifiers of keywords included in the preset keyword extractingpatterns in the preset fields. As such, after at least one presetkeyword extracting pattern which is in a preset field and used to parseuser-input speech data to generate semantic parsing information isobtained in step 100, the importance degree identifiers of keywords inthe keyword extracting patterns in the preset field may be obtained fromthe obtained keyword extracting patterns. Then, the subject weights ofkeywords are obtained according to the importance degree identifiers ofkeywords in the preset field.

102: calculating a subject score of speech parsing information accordingto the subject weights of the keywords;

103: recognizing whether the speech parsing information belongs to thepreset field according to the subject score of the speech parsinginformation.

For example, if the importance degree identifiers of keywords in thekeyword extracting patterns in the preset field are classified intothree levels: high, middle and low, correspondingly a correspondencerelationship between each level of importance degree identifier and acorresponding subject weight may be pre-stored in the apparatus ofrecognizing the field of the semantic parsing information. At this time,step 101 “obtaining subject weights of keyword according to theimportance degree identifiers of keywords in the keyword extractingpatterns in the preset field” may specifically include the followingcases:

(c1) if the importance degree identifier of a keyword in the presetkeyword extracting pattern in the preset field is high, obtaining a 0subject weight corresponding to high, according to the correspondencerelationship between the importance degree identifier in the presetfield and the subject weight;

(c2) if the importance degree identifier of a keyword in the presetkeyword extracting pattern in the preset field is middle, obtaining asubject weight corresponding to middle as a first prime number, forexample 2, according to the correspondence relationship between theimportance degree identifier in the preset field and the subject weight.In the present embodiment, according to the property of the prime numberhaving two common divisors, namely, 1 and itself, the prime number isused a weight to facilitate subsequently recognizing whether the speechparsing information belongs to the preset field according to the subjectscore of the speech parsing information.

(c3) if the importance degree identifier of a keyword in the presetkeyword extracting pattern in the preset field is low, obtaining asubject weight corresponding to low as a second prime number which isnot equal to the first prime number; for example, the second primenumber may be 3.

The importance degree of the keyword identified with high in the presetfield is higher than that of the keyword identified with middle in thepreset field; the importance degree of the keyword identified withmiddle in the preset field is higher than that of the keyword identifiedwith low in the preset field.

Further optionally, at this time, the step 102 “calculating a subjectscore of speech parsing information according to the subject weights ofthe keywords” may specifically comprise: multiplying subject weights ofkeywords, to obtain the subject score of the speech parsing information.For example, the score may be represented by the following formula:score=w(term1)*w(term2)* . . . *w(termN)

wherein score represents a subject score of the speech parsinginformation; w(term1) represents the first term of the current presetkeyword extracting pattern, namely, a subject weight of the firstkeyword; w(term2) represents the second term of the current presetkeyword extracting pattern, namely, a subject weight of the firstkeyword; w (termN) represents the N^(th) term of the current presetkeyword extracting pattern, namely, a subject weight of the firstkeyword. In the present embodiment, N terms are taken as an example.

Further optionally, at this time, step 103 “recognizing whether thespeech parsing information belongs to the preset field according to thesubject score of the speech parsing information” may specificallyinclude the following cases:

(d1) if the subject score of the speech parsing information is 0,determining that the speech parsing information belongs to the presetfield;

The subject score of the speech parsing information of the presentembodiment is obtained by multiplying subject weights of keywords, andthe subject weight of the keyword with a high importance degreeidentifier is 0. That is to say, if the preset keyword extractingpattern only includes a subject weight with a high importance degreeidentifier, the subject score of the speech parsing information is 0.Hence, if the subject score of the speech parsing information is 0, itmay be determined that the speech parsing information belongs to thepreset field.

(d2) if the subject score of the speech parsing information minus afirst parameter or a second parameter to get a remainder 0, determiningthat the speech parsing information includes a keyword with a middleimportance degree identifier and that the number of the includedkeywords is larger than 1, and determining that the speech parsinginformation belongs to the preset field; wherein the first parameter isequal to a square of the first prime number, and the second parameter isequal to a product of the first prime number and the second primenumber.

The first prime number and second prime number in the above embodimentonly include two common divisors, namely, itself and 1. At this time,the first prime number is multiplied with the first prime number toobtain the first parameter, namely, the first parameter is equal to thesquare of the first prime number. The first prime number is multipliedwith the second prime number to obtain the second parameter. Then, ifthe subject score of the speech parsing information minus the firstparameter to get a remainder 0, this indicates that the subject scoreincludes the first parameter obtained by multiplying the first primenumber with the first prime number, and indicates that the presetkeyword extracting pattern at least includes two keywords with themiddle importance degree identifier; if the subject score of the speechparsing information minus the second parameter to get a remainder 0,this indicates that the subject score includes the second parameterobtained by multiplying the first prime number with the second primenumber, and indicates that the preset keyword extracting pattern atleast includes a keywords with the middle importance degree identifierand a keyword with the low importance degree identifier. That is, it maybe determined that the speech parsing information includes the keywordwith the middle importance degree identifier, and the number of theincluded keywords is larger than 1, and it may be determined that thespeech parsing information belongs to the preset field.

(d3) if the subject score of the speech parsing information is not equalto 0 and the remainder resulting from the subject score minus the firstparameter or second parameter is not equal to 0, determining that thespeech parsing information does not belong to the preset field.

If the subject score of the speech parsing information does not satisfythe above cases (c1) and (c2), namely, at this time, the subject scoreof the speech parsing information is not equal to 0 and the remainderresulting from the subject score minus the first parameter or secondparameter is not equal to 0, it is determined that the speech parsinginformation does not belong to the preset field.

In addition, if the importance degree identifiers of keywords in thekeyword extracting pattern in the preset field are represented directlywith the probability of occurrence of keywords in the linguistic data inthe corpus, it is feasible to, at this time, set a corresponding weightaccording to a magnitude of the probability of occurrence of thekeywords in the linguistic data in the corpus. For example, the weightset at this time may be in a direct proportion to the probability. Alarger probability may be provided with a larger weight. For example, itis feasible to, in a similar manner, classify probabilities into 10levels, and set corresponding weights respectively as 10 positiveintegers in a range of 1-10. The level with a minimum probability isprovided with a smaller weight 1, a level with a maximum probability isprovided with a maximum weight 10, and other levels are provided with aweight in a similar way. Alternatively, it is also possible to directlyconsider values of probability of occurrence of keywords in thelinguistic data in the corpus as subject weights of the correspondingkeywords.

At this time, the subject score of the speech parsing information iscalculated according to the subject weights of keywords. The subjectscore of the speech parsing information may be obtained by adding up thesubject weights of keywords in the keyword extracting pattern. Forexample, the formula employed at this time may be represented asscore=w(term1)+w (term2)+ . . . +w (temrN). At this time, specifically,reference may be made to a preset threshold to recognize whether thespeech parsing information belongs to the preset field according to thesubject score of the speech parsing information. If the subject score ofthe speech parsing information is larger than or equal to a presetthreshold, this indicates that at this time the speech parsinginformation belongs to the preset field corresponding to the keywordextracting pattern. If the subject score of the speech parsinginformation is smaller than a preset threshold, this indicates that atthis time the speech parsing information does not belong to the presetfield corresponding to the keyword extracting pattern.

According to the method of recognizing the field of semantic parsinginformation of the present embodiment, at least one preset keywordextracting pattern which is in a preset field and used to parseuser-input speech data to generate semantic parsing information isobtained, wherein each of the at least one preset keyword extractingpattern comprises at least one keyword; subject weights of keywords areobtained according to the importance degree identifiers of keywords inthe keyword extracting patterns in the preset field; a subject score ofspeech parsing information is calculated according to the subjectweights of the keywords; whether the speech parsing information belongsto the preset field is recognized according to the subject score of thespeech parsing information. The technical solution of the presentembodiment may be employed to recognize the field to which the speechparsing information belongs to thereby ensure correctness of therecognized field of the speech parsing information, and thereby ensurecorrectness of operations performed by the App according to the semanticparsing information.

In conjunction with the characteristics of semantic parsing tasks, themethod of recognizing the field of semantic parsing information of thepresent embodiment mainly employs an idea of considering term subjectweights, and the method achieves a very good false rejection effectafter being tested on multiple fields of a universal semantic parsingplatform; the method has a good field transplantation performance andfacilitates optimization for application to a specific field.Furthermore, the method of recognizing the field of semantic parsinginformation of the present embodiment brings about a better parsingeffect, and can implement evaluation of reliability of a parsing resultwhen the semantic parsing information is applied in multiple verticalfields; furthermore, the test result indicates introduction of thismethod causes obviously positive benefits to the parsing results.

FIG. 2 is a structural diagram of a first embodiment of an apparatus ofrecognizing a field of semantic parsing information according to thepresent disclosure. As shown in FIG. 2, the apparatus of recognizing thefield of semantic parsing information of the present embodiment mayspecifically include: a pattern obtaining module 10, a subject weightobtaining module 11, a calculating module 12 and a recognizing module13.

The pattern obtaining module 10 is configured to obtain at least onepreset keyword extracting pattern which is in a preset field and used toparse user-input speech data to generate semantic parsing information,wherein each of the at least one preset keyword extracting patterncomprises at least one keyword;

the subject weight obtaining module 11 is configured to obtain subjectweights of keywords according to importance degree identifiers ofkeywords in the preset keyword extracting patterns obtained by thepattern obtaining module 10 in the preset field;

the calculating module 12 is configured to calculate a subject score ofspeech parsing information according to the subject weights of thekeywords obtained by the subject weight obtaining module 11;

the recognizing module 13 is configured to recognize whether the speechparsing information belongs to the preset field according to the subjectscore of the speech parsing information calculated by the calculatingmodule 12.

Principles employed by the apparatus of recognizing the field of thesemantic parsing information of the present embodiment to implementinformation processing with the above modules and the resultanttechnical effects are the same as those of the above-mentioned methodembodiments. For particulars, please refer to the depictions of theaforesaid relevant method embodiments, and no detailed depictions willbe presented here.

FIG. 3 is a structural diagram of a second embodiment of an apparatus ofrecognizing a field of semantic parsing information according to thepresent disclosure. As shown in FIG. 3, the apparatus of recognizing thefield of the semantic parsing information of the present embodiment, onthe basis of the technical solution of the embodiment shown in FIG. 2,further introduces the technical solution of the present disclosure inmore detail. As shown in FIG. 3, the apparatus of recognizing the fieldof the semantic parsing information according to the present embodimentfurther comprises:

a setting module 14 configured to set a plurality of preset keywordextracting patterns in each preset field, each preset keyword extractingpattern comprising at least two keywords;

an importance degree identifying module 15 configured to, in the presetkeyword extracting patterns of each preset field set by the settingmodule 14, identify importance degree identifiers of keywords includedin the corresponding preset keyword extracting patterns in thecorresponding preset field.

At this time, correspondingly, the pattern obtaining module 10 isconfigured to obtain, from the multiple preset keyword extractingpatterns of each preset field set by the setting module 14, the presetkeyword extracting patterns of the preset field, matched with thesemantic parsing information of the user-input speech data.

Further optionally, as shown in FIG. 3, the apparatus of recognizing thefield of semantic parsing information of the present embodiment furthercomprises:

an importance degree identifier obtaining module 16 configured to obtainimportance degree identifiers of keywords included in the preset keywordextracting patterns set by the setting module 14 in the correspondingpreset field.

For example, the importance degree identifier obtaining module 16 isspecifically configured to:

collect several linguistic data in each preset field and generate acorresponding corpus of the preset field;

perform word segmentation for linguistic data in the corpus, and extractvalid segmented words in the respective linguistic data as keywordsincluded by the linguistic data;

make statistics of a frequency of occurrence of each keyword in allkeywords obtained after word segmentation is performed for severallinguistic data in the corpus, as a word frequency of the correspondingkeyword in the corpus;

set an importance degree identifier in a preset field for acorresponding keyword according to a probability of the word frequencyof each keyword in the corpus in an occurrence frequency of all keywordsobtained after word segmentation is performed for several linguisticdata.

Then, correspondingly, the importance degree identifying module 15 isconfigured to use the importance degree identifiers of the keywords inthe preset field obtained by the importance degree identifier obtainingmodule 16 to, in the preset keyword extracting patterns of each presetfield set by the setting module 14, identify importance degreeidentifiers of keywords included in the corresponding preset keywordextracting patterns in the corresponding preset field.

Further optionally, in the apparatus of recognizing the field ofsemantic parsing information of the present embodiment, the subjectweight obtaining module 11 is specifically configured to:

if the importance degree identifier of a keyword in the preset keywordextracting pattern obtained by the pattern obtaining module 10 in thepreset field is high, obtain a 0 subject weight corresponding to high,according to a correspondence relationship between the importance degreeidentifier and the subject weight;

if the importance degree identifier of the keyword in the preset keywordextracting pattern obtained by the pattern obtaining module 10 in thepreset field is middle, obtain a subject weight corresponding to middleas a first prime number according to the correspondence relationshipbetween the importance degree identifier and the subject weight; or

if the importance degree identifier of the keyword in the preset keywordextracting pattern obtained by the pattern obtaining module 10 in thepreset field is low, obtain a subject weight corresponding to low as asecond prime number according to the correspondence relationship betweenthe importance degree identifier and the subject weight; the secondprime number is not equal to the first prime number; the importancedegree of the keyword identified with high in the preset field is higherthan that of the keyword identified with middle in the preset field; theimportance degree of the keyword identified with middle in the presetfield is higher than that of the keyword identified with low in thepreset field.

Further optionally, in the apparatus of recognizing the field ofsemantic parsing information of the present embodiment, the calculatingmodule 12 is specifically configured to:

multiply subject weights of keywords obtained by the subject weightextracting pattern 11, to obtain the subject score of the speech parsinginformation.

Further optionally, in the apparatus of recognizing the field ofsemantic parsing information of the present embodiment, the recognizingmodule 13 is specifically configured to:

if the subject score of the speech parsing information calculated by thecalculating module 12 is 0, determine that the speech parsinginformation belongs to the preset field; or if the subject score of thespeech parsing information calculated by the calculating module 12 minusa first parameter or a second parameter to get a remainder 0, determinethat the speech parsing information includes a keyword with a middleimportance degree identifier and that the number of the includedkeywords is larger than 1, and determine that the speech parsinginformation belongs to the preset field; wherein the first parameter isequal to a square of the first prime number, and the second parameter isequal to a product of the first prime number and the second primenumber; or

if the subject score of the speech parsing information calculated by thecalculating module 12 is not equal to 0 and the remainder resulting fromthe subject score minus the first parameter or second parameter is notequal to 0, determine that the speech parsing information does notbelong to the preset field.

Principles employed by the apparatus of recognizing the field of thesemantic parsing information of the present embodiment to implementinformation processing with the above modules and the resultanttechnical effects are the same as those of the above-mentioned methodembodiments. For particulars, please refer to the depictions of theaforesaid relevant method embodiments, and no detailed depictions willbe presented here.

FIG. 4 is a structural diagram of an embodiment of a computer deviceaccording to the present disclosure. As shown in FIG. 4, the computerdevice according to the present embodiment comprises: one or moreprocessors 30, and a memory 40 for storing one or more programs, the oneor more programs stored in the memory 40, when executed by said one ormore processors 30, enabling said one or more processors 30 to implementthe method of recognizing the field of the semantic parsing informationof the embodiments as shown in FIG. 1-FIG. 3. The embodiment shown inFIG. 4 exemplarily includes a plurality of processors 30.

For example, FIG. 5 is an example diagram of a computer device accordingto the present disclosure. FIG. 5 shows a block diagram of an examplecomputer device 12 a adapted to implement an implementation mode of thepresent disclosure. The computer device 12 a shown in FIG. 5 is only anexample and should not bring about any limitation to the function andscope of use of the embodiments of the present disclosure.

As shown in FIG. 5, the computer device 12 a is shown in the form of ageneral-purpose computing device. The components of computer device 12 amay include, but are not limited to, one or more processors 16 a, asystem memory 28 a, and a bus 18 a that couples various systemcomponents including the system memory 28 a and the processors 16 a.

Bus 18 a represents one or more of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer device 12 a typically includes a variety of computer systemreadable media. Such media may be any available media that is accessibleby computer device 12 a, and it includes both volatile and non-volatilemedia, removable and non-removable media.

The system memory 28 a can include computer system readable media in theform of volatile memory, such as random access memory (RAM) 30 a and/orcache memory 32 a. Computer device 12 a may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 a can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown in FIG. 5 and typically called a “hard drive”). Although notshown in FIG. 5, a magnetic disk drive for reading from and writing to aremovable, non-volatile magnetic disk (e.g., a “floppy disk”), and anoptical disk drive for reading from or writing to a removable,non-volatile optical disk such as a CD-ROM, DVD-ROM or other opticalmedia can be provided. In such instances, each drive can be connected tobus 18 a by one or more data media interfaces. The system memory 28 amay include at least one program product having a set (e.g., at leastone) of program modules that are configured to carry out the functionsof embodiments shown in FIG. 1-FIG. 3 of the present disclosure.

Program/utility 40 a, having a set (at least one) of program modules 42a, may be stored in the system memory 28 a by way of example, and notlimitation, as well as an operating system, one or more disclosureprograms, other program modules, and program data. Each of theseexamples or a certain combination thereof might include animplementation of a networking environment. Program modules 42 agenerally carry out the functions and/or methodologies of embodimentsshown in FIG. 1-FIG. 3 of the present disclosure.

Computer device 12 a may also communicate with one or more externaldevices 14 a such as a keyboard, a pointing device, a display 24 a,etc.; with one or more devices that enable a user to interact withcomputer device 12 a; and/or with any devices (e.g., network card,modem, etc.) that enable computer device 12 a to communicate with one ormore other computing devices. Such communication can occur viaInput/Output (I/O) interfaces 22 a. Still yet, computer device 12 a cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20 a. As depicted in FIG. 5, networkadapter 20 a communicates with the other communication modules ofcomputer device 12 a via bus 18 a. It should be understood that althoughnot shown, other hardware and/or software modules could be used inconjunction with computer device 12 a. Examples, include, but are notlimited to: microcode, device drivers, redundant processing units,external disk drive arrays, RAID systems, tape drives, and data archivalstorage systems, etc.

The processor 16 a executes various function applications and dataprocessing by running programs stored in the system memory 28 a, forexample, implements the method of recognizing the field of semanticparsing information shown in the above embodiments.

The present disclosure further provides a computer readable medium onwhich a computer program is stored, the program, when executed by aprocessor, implementing the method of recognizing the field of semanticparsing information shown in the above embodiments.

The computer readable medium of the present embodiment may include RAM30 a, and/or cache memory 32 a and/or a storage system 34 a in thesystem memory 28 a in the embodiment shown in FIG. 5.

As science and technology develops, a propagation channel of thecomputer program is no longer limited to tangible medium, and it mayalso be directly downloaded from the network or obtained in othermanners. Therefore, the computer readable medium in the presentembodiment may include a tangible medium as well as an intangiblemedium.

The computer-readable medium of the present embodiment may employ anycombinations of one or more computer-readable media. The machinereadable medium may be a machine readable signal medium or a machinereadable storage medium. A machine readable medium may include, but notlimited to, an electronic, magnetic, optical, electromagnetic, infrared,or semiconductor system, apparatus, or device, or any suitablecombination of the foregoing. More specific examples of the machinereadable storage medium would include an electrical connection havingone or more wires, a portable computer diskette, a hard disk, a randomaccess memory (RAM), a read-only memory (ROM), an erasable programmableread-only memory (EPROM or Flash memory), a portable compact discread-only memory (CD-ROM), an optical storage device, a magnetic storagedevice, or any suitable combination of the foregoing. In the textherein, the computer readable storage medium can be any tangible mediumthat include or store programs for use by an instruction executionsystem, apparatus or device or a combination thereof.

The computer-readable signal medium may be included in a baseband orserve as a data signal propagated by part of a carrier, and it carries acomputer-readable program code therein. Such propagated data signal maytake many forms, including, but not limited to, electromagnetic signal,optical signal or any suitable combinations thereof. Thecomputer-readable signal medium may further be any computer-readablemedium besides the computer-readable storage medium, and thecomputer-readable medium may send, propagate or transmit a program foruse by an instruction execution system, apparatus or device or acombination thereof.

The program codes included by the computer-readable medium may betransmitted with any suitable medium, including, but not limited toradio, electric wire, optical cable, RF or the like, or any suitablecombination thereof.

Computer program code for carrying out operations disclosed herein maybe written in one or more programming languages or any combinationthereof. These programming languages include an object orientedprogramming language such as Java, Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The program codemay execute entirely on the user's computer, partly on the user'scomputer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider).

In the embodiments provided by the present disclosure, it should beunderstood that the revealed system, apparatus and method can beimplemented in other ways. For example, the above-described embodimentsfor the apparatus are only exemplary, e.g., the division of the units ismerely logical one, and, in reality, they can be divided in other waysupon implementation.

The units described as separate parts may be or may not be physicallyseparated, the parts shown as units may be or may not be physical units,i.e., they can be located in one place, or distributed in a plurality ofnetwork units. One can select some or all the units to achieve thepurpose of the embodiment according to the actual needs.

Further, in the embodiments of the present disclosure, functional unitscan be integrated in one processing unit, or they can be separatephysical presences; or two or more units can be integrated in one unit.The integrated unit described above can be implemented in the form ofhardware, or they can be implemented with hardware plus softwarefunctional units.

The aforementioned integrated unit in the form of software functionunits may be stored in a computer readable storage medium. Theaforementioned software function units are stored in a storage medium,including several instructions to instruct a computer device (a personalcomputer, server, or network equipment, etc.) or processor to performsome steps of the method described in the various embodiments of thepresent disclosure. The aforementioned storage medium includes variousmedia that may store program codes, such as U disk, removable hard disk,Read-Only Memory (ROM), a Random Access Memory (RAM), magnetic disk, oran optical disk.

What are stated above are only preferred embodiments of the presentdisclosure and not intended to limit the present disclosure. Anymodifications, equivalent substitutions and improvements made within thespirit and principle of the present disclosure all should be included inthe extent of protection of the present disclosure.

What is claimed is:
 1. A method of recognizing a field of semanticparsing information, wherein the method comprises: obtaining at leastone preset keyword extracting template which is in a preset field andused to parse user-input speech data to generate semantic parsinginformation, each of the at least one preset keyword extracting templatecomprising at least one keyword; obtaining a subject weight of each ofthe at least one keyword according to an importance degree identifier ofthe keyword in the preset keyword extracting template in the presetfield; wherein the subject weight of a keyword, whose importance degreeidentifier is high, is identified with 0, the subject weight of akeyword, whose importance degree identifier is middle, is identifiedwith a first prime number, and the subject weight of a keyword, whoseimportance degree identifier is low, is identified with a second primenumber different from the first prime number; and wherein the importancedegree of the keyword identified with high in the preset field is higherthan that of the keyword identified with middle in the preset field, andthe importance degree of the keyword identified with middle in thepreset field is higher than that of the keyword identified with low inthe preset field; calculating a subject score of the semantic parsinginformation by multiplying the subject weight of each of at least twokeywords in case that there are at lest two keywords, and taking thesubject weight of one keyword as the subject score of the semanticparing information in case that there is the one keyword; determiningthat the semantic parsing information, whose subject score is 0, belongsto the preset field; the first parameter is equal to a square of thefirst prime number, and the second parameter is determining that thesemantic parsing information, whose subject score minus a firstparameter or a second parameter to get a remainder 0, belongs to thepreset field, wherein the first parameter is equal to a square of thefirst prime number, and the second parameter is equal to a product ofthe first prime number and the second prime number; and determining thatthe semantic parsing information, whose subject score is not equal to 0and the remainder resulting from the subject score minus the firstparameter or second parameter is not equal to 0, does not belong to thepreset field.
 2. The method according to claim 1, wherein beforeobtaining at least one preset keyword extracting template which is in apreset field and used to parse user-input speech data to generatesemantic parsing information, the method further comprises: setting aplurality of preset keyword extracting templates in each said presetfield, each of the preset keyword extracting templates comprising atleast two keywords; and in the preset keyword extracting templates ofeach said preset field, identifying importance degree identifiers of thekeywords included in the preset keyword extracting templates in thepreset field.
 3. The method according to claim 2, wherein beforeobtaining the subject weight of each of the at least one keywordaccording to the importance degree identifier of the keyword in thepreset keyword extracting template in the preset field, the methodfurther comprises: obtaining the importance degree identifier of eachkeyword included in the preset keyword extracting templates in thepreset field.
 4. The method according to claim 3, wherein obtaining theimportance degree identifier of each keyword included in the presetkeyword extracting template in the preset field specifically comprises:collecting linguistic data in the each preset field and generating acorpus of the preset field; performing word segmentation for linguisticdata in the corpus, and extracting valid segmented words in thelinguistic data as the keywords included by the linguistic data; makingstatistics of a frequency of occurrence of a keyword in all keywordsobtained after word segmentation is performed for the linguistic data inthe corpus, as a word frequency of the keyword in the corpus; andsetting an importance degree identifier in the preset field for thekeyword according to a probability of the word frequency of the keywordin the corpus in an occurrence frequency of all the keywords obtainedafter word segmentation is performed for the linguistic data.
 5. Acomputer device, wherein the device comprises: one or more processors, amemory for storing one or more programs, the one or more programs, whenexecuted by said one or more processors, enabling said one or moreprocessors to implement the following operation: obtaining at least onepreset keyword extracting template which is in a preset field and usedto parse user-input speech data to generate semantic parsinginformation, each of the at least one preset keyword extracting templatecomprising at least one keyword; obtaining a subject weight of each ofthe at least one keyword according to an importance degree identifier ofthe keyword in the preset keyword extracting template in the presetfield; wherein the subject weight of a keyword, whose importance degreeidentifier is high, is identified with 0, the subject weight of akeyword, whose importance degree identifier is middle, is identifiedwith a first prime number, and the subject weight of a keyword, whoseimportance degree identifier is low. is identified with a second primenumber different from the first prime number, and wherein the importancedegree of the keyword identified with high in the preset field is higherthan that of the keyword identified with middle in the preset field; andthe importance degree of the keyword identified with middle in thepreset field is higher than that of the keyword identified with low inthe preset field; calculating a subject score of the semantic parsinginformation by multiplying the subject weight of each of at least twokeywords in case that there are at least two keywords, and taking thesubject weight of one keyword as the subject score of the semanticparing information in case that there is the one keyword; determiningthat the semantic parsing information, whose subject score is 0, belongsto the preset field, determining that the semantic parsing information,whose subject score minus a first parameter or a second parameter to geta remainder 0, belongs to the preset field, wherein the first parameteris equal to a square of the first prime number, and the second parameteris equal to a product of the first prime number and the second primenumber, and determining that the semantic parsing information, whosesubject score is not equal to 0 and the remainder resulting from thesubject score minus the first parameter or second parameter is not equalto 0, does not belong to the preset field.
 6. The computer deviceaccording to claim 5, wherein before obtaining at least one presetkeyword extracting template which is in a preset field and used to parseuser-input speech data to generate semantic parsing information, theoperation further comprises: setting a plurality of preset keywordextracting templates in each said preset field, each of the presetkeyword extracting templates comprising at least two keywords; and inthe preset keyword extracting templates of each said preset field,identifying importance degree identifiers of the keywords included inthe preset keyword extracting templates in the preset field.
 7. Thecomputer device according to claim 6, wherein before obtaining thesubject weight of each of the at least one keyword according to theimportance degree identifier of the keyword in the preset keywordextracting, template in the preset field, the operation furthercomprises: obtaining the importance degree identifiers of each includedin the preset keyword extracting templates in the preset field.
 8. Thecomputer device according to claim 7, wherein obtaining the importancedegree identifier of each keyword included in the preset keywordextracting template in the preset field specifically comprises:collecting linguistic data in the each preset field and generating acorpus of the preset field; performing word segmentation for linguisticdata in the corpus, and extracting valid segmented words in thelinguistic data as the keywords included by the linguistic data; makingstatistics of a frequency of occurrence of a keyword in all keywordsobtained after word segmentation is performed for the linguistic data inthe corpus, as a word frequency of the keyword in the corpus; andsetting an importance degree identifier in the preset field for thekeyword according to a probability of the word frequency of the keywordin the corpus in an occurrence frequency of all the keywords obtainedafter word segmentation is performed for the linguistic data.
 9. Anon-transitory computer readable medium on which a computer program isstored, wherein the program, when executed by a processor, implementsthe following operation: obtaining at least one preset keywordextracting template which is in a preset field and used to parseuser-input speech data to generate semantic parsing information, each ofthe at least one preset keyword extracting template comprising at leastone keyword; obtaining a subject weight of each of the at least onekeyword according to an importance degree identifier of the keyword inthe preset keyword extracting template in the preset field; wherein thesubject weight of a keyword, whose importance degree identifier is high,is identified with 0, the subject weight of a keyword, whose importancedegree identifier is middle is identified with a first middle number,and the subject weight of a keyword, whose importance degree identifieris low, is identified with a second prime number different from thefirst prime number; and wherein the importance degree of the keywordidentified with high in the preset field is higher than that of thekeyword identified with middle in the preset field; and the importancedegree of the keyword identified with middle in the preset field ishigher than that of the keyword identified with low in the preset field;calculating a subject score of the semantic parsing information bymultiplying the subject weight of each of at least two keywords in casethat there are at least two keywords, and taking the subject weight ofone keyword as the subject score of the semantic paring information incase that there is the one keyword; determining that the semanticparsing information, whose subject score is 0, belongs to the presetfield; determining that the semantic parsing information, whose subjectscore minus a first parameter or a second parameter to get a remainder0, belongs to the preset field, wherein the first parameter is equal toa square of the first prime number, and the second parameter is equal toa product of the first prime number and the second prime number; anddetermining that the semantic parsing information, whose subject scoreis not equal to 0 and the remainder resulting from the subject scoreminus the first parameter or second parameter is not equal to 0, doesnot belong to the preset field.
 10. The non-transitory computer readablemedium according to claim 9, wherein before obtaining at least onepreset keyword extracting template which is in a preset field and usedto parse user-input speech data to generate semantic parsinginformation, the operation further comprises: setting a plurality ofpreset keyword extracting templates in each said preset field, each ofthe preset keyword extracting templates comprising at least twokeywords; and in the preset keyword extracting templates of each saidpreset field, identifying importance degree identifiers of the keywordsincluded in the preset keyword extracting templates in the preset field.11. The non-transitory computer readable medium according to claim 10,wherein before obtaining the subject weight of each of the at least onekeyword according to the importance degree identifier of the keyword inthe preset keyword extracting template in the preset field, theoperation further comprises: obtaining the importance degree identifierof each keyword included in the preset keyword extracting templates inthe preset field.
 12. The non-transitory computer readable mediumaccording to claim 11, wherein obtaining the importance degreeidentifier of each keyword included in the preset keyword extractingtemplates in the preset field specifically comprises: collectinglinguistic data in the each preset field and generating a corpus of thepreset field; performing word segmentation for linguistic data in thecorpus, and extracting valid segmented words in the linguistic data asthe keywords included by the linguistic data; making statistics of afrequency of occurrence of a keyword in all keywords obtained after wordsegmentation is performed for the linguistic data in the corpus, as aword frequency of the keyword in the corpus; and setting an importancedegree identifier in the preset field for the keyword according to aprobability of the word frequency of the keyword in the corpus in anoccurrence frequency of all the keywords obtained after wordsegmentation is performed for the linguistic data.